Abstract
Text of abstract
Temperate forests in the Upper Midwest are important. {C sequestration}. {Other ecosystem services}. However, the fate of these forests is highly uncertain. The natural ecological succession of these forests has been significantly altered by the interactions of a significantly different and rapidly changing climate, and novel disturbance regimes under which some natural disturbances (e.g. fire) are suppressed while others (e.g. harvest; pests and pathogens) may be becoming more frequent. {UMBS ecological background/natural history?} Therefore, predictions about the future of these forests based on past analogs may not be reliable.
Instead, more reliable predictions can be obtained by using dynamic vegetation models that explicitly represent processes involved in forest growth and mortality. Vegetation models fall broadly along the following spectrum of complexity. On one side are relatively simple “Big leaf” models—such as PNET, SiBCASA, and Biome-BGC —in which the vegetation at a particular location consists essentially of a single large “plant” whose characteristics are the (weighted) average of all the vegetation at that site. These models were designed originally to simulate the land surface boundary condition for atmospheric general circulation models, though similar models continue to be applied successfully to simulate biogeochemical processes in many ecosystem science contexts. On the other end of the spectrum are “individual-based” models (a.k.a. “gap models”), which explicitly simulate multiple indivduals competing for resources at a single site (Shugart et al. 2015). Examples of such models are LANDIS and UVAFME. Because these models can explicitly represent inter-specific differences in plant productivity, resource allocation, and stress tolerance (among others), as well as the competition that emerges out of these differences, they may be better able to represent changes in ecosystem-scale processes, especially in no-analog conditions (Purves and Pacala 2008). Moreover, these models’ predictions of species composition and ecosystem structure are relevant outside of carbon cycle science, particularly for forest management and wildlife conservation. However, these benefits of individual-based models come at the cost of much higher data and computational requirements, which effectively restrict their application to individual, well-studied sites. Therefore, a key objective of vegetation modeling research over the last several decades has been the development of approaches that capture the emergent biogeochemical and ecological outcomes of interactions between individual plants without the need for explicitly simulating each individual (Purves and Pacala 2008). In this study, we focus on the Ecosystem Demography model (ED2.2; Moorcroft et al. 2001, Medvigy and Moorcroft 2011, Longo et al. 2019b), which constitutes an example of such an approach.
Vegetation model projections are inherently uncertain, and this uncertainty stems from several sources. Driver uncertainty refers to uncertainty in data about processes that are essential to the system in question but are not represented by the model (e.g. weather and climate for vegetation models). Initial condition uncertainty arises from the fact that models have to start somewhere, and the exact conditions at the place and time simulations begin are uncertain. Process or structural uncertainty arises because vegetation models necessarily only represent a subset of all processes involved in plant biology, and that the process that are included can usually be represented in multiple different ways (i.e. which processes are included, and how they are represented). Finally, parameter uncertainty arises because of natural variability and imperfect calibration of the above process representations.
Several recent studies have looked at parameter uncertainty in ED2. Dietze et al. (2014) evaluated which ED2 parameters contributed the most to uncertainties in ED2 simulations of productivity across multiple biomes in North America at 1, 5, and 10 year time scales. They found that the parameters contributing the most to model uncertainty across all biomes were those related to growth respiration, mortality, stomatal conductance, and water uptake. However, they also found that despite some common patterns, parameter uncertainty varied with biome. More recently, a similar analysis evaluated parameter uncertainty over 100 year time scale at the Willow Creek Ameriflux site, and found that the most important parameters to model uncertainty were quantum efficiency of photosynthesis, leaf respiration, and soil-plant water transfer (Raczka et al. 2018). Another recent study assessed parameter uncertainty in ED2 as part of its overall goal of parameterizing the model for sagebrush (Pandit et al. 2018). They found that the most important parameters were specific leaf area, the maximum rate of carbon fixation (Vcmax), slope of stomatal conductance, the fine-root to leaf carbon ratio, and the fine root turnover rate. Another study looked specifically at contributions to uncertainty from parameters related to canopy radiative transfer, and found that parameters related to both canopy structure and leaf optical properties had a large (>20%) impact on predictions of net primary productivity (Viskari et al. 2019). The variability in results across these studies demonstrates that parameter contributions to ED2 uncertainty are highly variable, and that additional parameter uncertainty studies are needed.
While parameter uncertainty ED2 has been previously evaluated, less work has been done on structural uncertainty. Numerous vegetation model intercomparison studies have demonstrated that different models produce significantly different projections of overall land carbon sequestration (Friedlingstein et al. 2006, 2014), response to CO2 fertilization (REF - Medlyn?), high-latitude warming (Rogers and Serbin?), {others…}. These differences have been attributed to differences in model representations of key processes, including canopy radiative transfer (Fisher et al. 2017) and photosynthesis (Rogers et al. 2016). However, the extent to which specific processes contribute to model uncertainty is difficult to evaluate from model intercomparisons because different models are different from each other in too many different ways. In this study, we evaluate structural uncertainty more precisely/directly by selectively toggling and switching individual processes/model components in ED2 while keeping everything else constant.
The focus of this study is on the interaction between parametric and structural uncertainty in the ED2 model. Driver uncertainty is outside the scope of this study, and initial condition uncertainty is minimized by our experimental design, which is conditioned on a specific initial condition (near bare ground). Our study is organized around the following guiding questions: (1) Which processes related to light utilization are most important to accurately modeling community succession and C cycle dynamics in temperate forests of the Upper Midwest? (2) What is the cost of considering these processes, in terms of additional parameteric uncertainty? (3) What are the relative contributions of parameteric uncertainty (data limitation) vs. structural uncertainty (theoretical limitation?)? To answer these questions, we ran ED2 ensemble simulationes with a factorial combination of submodels related to radiative transfer formulation (two-stream vs. multiple scatter), horizontal competition (finite canopy radius vs. complete shading), and trait plasticity (whether or not SLA and Vcmax vary with light level) We then used a sensitivity and variance decomposition analysis to evaluate the contribution of parameter uncertainty for each model configuration.
We performed this study at the University of Michigan Biological Station (UMBS; Ameriflux site US-UMd, 45.5625\(^{\circ}\), -84.6975\(^{\circ}\)), located in Northern Lower Michigan, USA. The area surrounding the research station is 87% well-drained upland forest and 13% wetland (Bergen and Dronova 2007); the focus of this study is on the former. The landscape geography of the UMBS upland forest is 20.4% moraine, 37.8% high outwash plain, 31.3% low outwash plain, 5.7% lake-margin terrace, 3.6% ice-contact, and 1.2% lowland glacial lake (Bergen and Dronova 2007). Most of the UMBS upland forest canopy is dominated by temperate deciduous early-successional species, most importantly Populus grandidentata (bigtooth aspen) and, to a lesser extent, Betula papyrifera (paper birch), with Acer saccharum (sugar maple), Acer rubrum (red maple), Fagus grandifolia (American beech), Tilia americana (basswood), Betula alleghaniensis (yellow birch), Fraxinus americana (white ash), Tsuga canadensis (eastern hemlock), Quercus rubra (northern red oak), Pinus strobus (white pine), and Pinus resinosa (red pine) existing in various fractions in the understory (and, in patches, in the canopy). This composition is a legacy of the site’s disturbance history: The site was intensively logged in the late 1800s and early 1900s, and experienced regularly recurring fires until the mid-1920s, at which point a regime of active fire suppression started that has persisted to the present day. As a result, the average stand age in 2013 was 95 years. The majority of the forest that is aspen-dominated is undergoing succession to “northern hardwood” (maple, beech, basswood, birch, ash, hemlock), “upland conifer” (red and white pine), or “northern red-oak” ecotypes (Bergen and Dronova 2007).
Forest stands at UMBS were previously exposed to experimental disturbance as part of the Forest Accelerated Succession Experiment (FASET).
For this study, we used the Ecosystem Demography Model, version 2.2 (ED2). Below, we provide only a high-level description of key model features and focus in detail only on submodels and configurations specific to this study. A full description of the default configuration of the model is provided by Longo et al. (2019b).
The key feature of the “ecosystem demography” approach underlying the ED2 model is a system of partial differential equations that simulate the emergent mean behavior of “cohorts” of individuals of similar composition, size, and age (Moorcroft et al. 2001). The original “Ecosystem Demography” (ED) model (Moorcroft et al. 2001) was enhanced by Medvigy et al. (2009) (as ED2) with fully-coupled leaf physiology, canopy radiative transfer, and micrometeorology, and each of these components have been further refined over the last decade (culminating in ED2.2; Longo et al. 2019b). Various versions of the ED model have been validated in boreal (Medvigy and Moorcroft 2011), temperate (Medvigy et al. 2009, Dietze et al. 2014, Raczka et al. 2018), and tropical biomes (Moorcroft et al. 2001, Longo et al. 2019a), and have been applied in:
ED2 represents both spatial and temporal variability hierarchically. The largest spatial unit is the “polygon”, within which all elements share the same meteorological forcings. Polygons are divided into multiple “sites”, each site with its own set of abiotic characteristics such as soil texture and topography. Sites are divided into discrete “patches” according to time since last disturbance. Finally, each patch is composed of an arbitrary (and dynamic) number of “cohorts”, representing the mean behavior of groups of plant individuals of similar size and composition. Temporal variability in ED2 is similarly hierarchical. Thermodynamics, including energy, water, and eddy (e.g. CO2) fluxes, are resolved on the order of seconds. Biophysics, including photosynthesis, respiration, and radiative transfer, and the evaluation of energy, water, and CO2 budgets, are resolved on the order of minutes. Phenology and plant carbon balance are evaluated daily. Cohort dynamics, including structural growth, mortality, and reproduction, are evaluated monthly. Finally, patch dynamics (e.g. annual disturbances) are evaluated annually. In this study, we consider only a single patch (and, therefore, a single site and polygon) starting from a “near bare ground” condition.
{Brief summary of thermodynamics?} {Brief summary of water cycle?}
ED2 represents the carbon flux for each cohort as the net sum of contributions from turbulent mixing, leaf photosynthesis and respiration, (fine) root respiration, growth and maintenance respiration, and heterotrophic respiration. In addition, each cohort also has a virtual carbon balance that links fast processes like photosynthesis and respiration to slow processes like allocation (to growth, storage, and reproduction) and mortality.
The official ED2 source code is available at https://github.com/EDmodel/ED2, and the exact version used in this study (which includes minor revisions to accommodate the requirements of this study) can be obtained https://github.com/ashiklom/ED2/tree/b048950971e91699b78fc566df133caf977fda32.
In this study, we ran a factorial combination of the following ED2 configurations: (1) two-stream vs. multiple-scatter canopy radiative transfer models; (2) infinite vs. finite crown area (a.k.a. complete vs. partial shading); and (3) static vs. light-plastic traits.
Both canopy radiative transfer models in ED2 resolve the full vertical radiation profile within a patch as a function of canopy structure (leaf and wood area indices, crown area, leaf angle distribution) and incident solar radiation. The two models differ in the mathematical approximations of the underlying differential radiative transfer equations.
The crown area submodel in ED2 determines the nature of competition for light between cohorts. In the default configuration (“infinite crown area”, or “complete shading”), the leaf area of a cohort is distributed across the entire horizontal area of a patch. This means that taller cohorts always receive more incoming radiation than shorter cohorts, even when the height difference is small. Put another way, this means that there is no horizontal competition, only vertical. This has been shown to excessively suppress competition from sub-dominant individuals and result in unrealistically homogeneous canopies (Fisher et al. 2015). In the alternate configuration (“finite crown area”, or “partial shading”), canopies take up only a fraction of the available horizontal area, meaning that multiple cohorts of similar height can receive the same level of light. The horizontal area of crowns is determined by allometric equations from Dietze and Clark (2008).
The third submodel we evaluated was trait plasticity. In the default configuration, all cohorts of a given plant functional type will have the same parameters, regardless of environmental conditions. This ignores the globally-documented intraspecific trait variability as a function of light level (Niinemets 2010, Keenan and Niinemets 2016). In the alternate configuration, as light level decreases (trees become more shaded), specific leaf area increases and Vcmax decreases, following empirical relationships from the tropics (Lloyd et al. 2010).
These sub-models are summarized in Table X.
| Name | Description | Color |
|---|---|---|
| Crown model | ||
closed (default) |
Cohort crowns take up entire patch area. Competition for light based only on height. | Light |
finite |
Cohort crown area is proportional to DBH according to PFT-specific allometry. | Dark |
| Radiative transfer model | ||
two-stream (default) |
Two-stream approximation, as in CLM 4.5 | Primary (red, blue) |
multi-scatter |
Multiple-scatter approximation, following (Zhao and Qualls 2005) | Secondary (green, orange) |
| Trait plasticity | ||
static (default) |
SLA and Vcmax are constant | Cool (blue, green) |
plastic |
SLA increases, and Vcmax decreases, with light level | Warm (red, orange) |
The full list of parameters in ED2 is large, with over 100 parameters per plant functional type. A full sensitivity and uncertainty analysis across all of these parameters is outside the scope of this study. Instead, we selected a subset of parameters that were identified as important by previous ED2 sensitivity studies (Dietze et al. 2014, Raczka et al. 2018, Viskari et al. 2019).
Three parameters are related to leaf-level physiology: Following the enzyme-kinetic model of Farquhar et al. (1980), the rate of photosynthesis is the minimum of light-limited and enzyme-limited reactions. The former are controlled by the quantum efficiency parameter—maximum efficiency with which absorbed photosynthetically active radiation is converted to CO2. The latter are controlled by Vcmax, the maximum rate of carbon fixation by Rubisco. The water demand of photosynthesis is controlled by the stomatal slope, the sensitivity of stomatal conductance of CO2 as a function of CO2 concentration and humidity at the leaf surface (Ball et al. 1987, Leuning 1995). Three more parameters correspond to the respiration rates of leaves, roots, and “growth maintenance”. Two more parameters control carbon allocation: One is the ratio of fine root to leaf biomass (fineroot2leaf, or q), and another is the ratio of “storage” carbon allocated to reproduction (r_fract). Three parameters control various aspects of adult tree mortality, namely the density-independent mortality rate (mort3), and the time scale (mort1) and critical carbon balance (mort2) of mortality from carbon starvation. An additional parameter controls seedling mortality. Specific leaf area (SLA) is used to convert leaf biomass to leaf area index, which in turn is used in a variety of calculations related to canopy radiative transfer and micrometeorology. Two additional parameters describe canopy structure in ways critical to the canopy radiative transfer: Canopy clumping factor describes how evenly leaf area is distributed in horizontal space (1 being perfectly evenly; 0 being a “black hole” where all leaves are concentrated in a single point); and leaf orientation factor describes the average distribution of leaf angles (-1 being perfectly vertical, 1 being perfectly horizontal, and 0 being random). Four parameters control leaf optical properties, namely the fractions of light reflected or transmitted in visible and near-infrared wavelengths (leaf_(reflect|trans)_(vis|nir)). Three other parameters:
f_labile)minimum_height)water_conductance)By default, ED2 supports 17 different plant functional types, which divide plant species according to photosynthetic pathway (C3 vs. C4), growth form (grass vs. tree), leaf phenology habit (deciduous vs. evergreen), biome (e.g. temperate vs. tropical), and successional status (e.g. early, mid, late). However, we limited our simulations to the four plant functional types that have any appreciable presence at UMBS: Early, mid, and late temperate deciduous trees and pines. The species comprising these plant functional types are shown in Table X.
| Plant functional type | Species | Color |
|---|---|---|
| Early hardwood | Betula papyrifera | Violet |
| Populus grandidentata | ||
| Populus tremuloides | ||
| Mid hardwood | Quercus rubra | Blue |
| Acer rubrum | ||
| Acer pensylvaticum | ||
| Late hardwood | Acer saccharum | Green |
| Fagus grandifolia | ||
| Pine | Pinus strobus | Yellow |
For each plant functional type, we generated a distribution of parameter values via a trait-meta analysis (LeBauer et al. 2013, see also Dietze et al. 2014, Raczka et al. 2018). Prior distributions for this meta-analysis were the same as those used in Dietze et al. (2014), Raczka et al. (2018), and Viskari et al. (2019). Species trait data for this meta-analysis came from existing records in the BETY database (www.betydb.org, LeBauer et al. 2017), as well as from publicly available records in the TRY database (www.try-db.org, Kattge et al. 2011) and, specifically for leaf optical properties, from Shiklomanov (2019 {dissertation, chapter 3}). The resulting parameter distributions are shown in Figure X. All parameters not described in this section were set to their ED2 PFT-specific defaults.
Figure 1: Input parameter distributions from PEcAn trait meta-analysis.
For each factorial combination of model configurations/submodels (described above), we ran 100 ensemble members from 1901 to 2000. Each ensemble member was initialized from ED2’s “near-bare ground” condition: An equal number of seedlings of each plant functional type (see previous section) at the minimum resolvable size. Driving meteorological data was 6-hourly CRU-NCEP combined with an annual atmospheric CO2 record from Law-Dome ice core (Etheridge et al. 1998) and Mauna Loa observatory (Thoning et al. 1989). Soil texture was set to 92% sand, 7% silt, and 1% clay, per site-level observations in (Gough et al. 2010). The initial soil moisture profile was set to the average soil moisture profile reported in the UMBS Ameriflux ancillary data (https://ameriflux.lbl.gov/sites/siteinfo/US-UMd). The entire model execution workflow was conducted using the Predictive Ecosystem Analyzer (PEcAn, www.pecanproject.org).
Figure 2: ED2 plot-level ensemble predictions of gross (GPP) and net (NPP) primary productivity, total leaf area index (LAI), and aboveground biomass (AGB) by model configuration. Individual ensemble members are shown as gray lines. The solid line is a LOESS regression (ggplot2::stat_smooth). Shading represents the middle 80% (10th to 90th percentile) of predictions. Black dot with error bars is the mean and min/max value from Hardiman et al. (2013).
Model projections of gross and net primary productivity, aboveground biomass, and leaf area index varied substantially with both parameter value and model structure (Figure 2). For every model structural configuration, there were multiple ensemble members that experienced total ecosystem collapse before the end of the simulation. However, model runs with finite canopy radius and two-stream radiative transfer had the highest failure rate, with most ensemble members collapsing almost immediately and the remaining few members showing relatively low productivity that declined to nearly zero. The remaining model configurations all followed a similar trend of rapid growth in the first simulation decade followed by gradual decline through the rest of the simulation (with individual ensemble members sometimes undergoing sudden collapse in specific years). The ensemble means of these configurations had very similar ecosystem states at peak productivity and at the end of the simulation, as well as the pace of initial rapid growth. That being said, ensemble means from model configurations that included finite canopy radius and multiple-scatter radiative transfer sustained peak aboveground biomass and leaf area indices for longer than other configurations, though this was not true of gross or mean primary productivity.
For most model configurations, the ensemble distribution of net primary productivity at the end of the run overlapped observations by Hardiman et al. (2013), with an ensemble mean that was slightly lower. (The exceptions were model configurations that included the combination of finite canopy radius and two-stream radiative transfer, which consistently underestimated productivity). On the other hand, ensemble predictions of leaf area index were consistently lower than observations, with only a subset of individual ensemble members across all configurations even overlapping the lower bounds of the observations. In fact, most ensemble members across all configurations failed to reach the observed leaf area index even at their peak. A subset of specific parameter combinations from model configurations that included finite canopy radius and multiple-scatter radiative transfer accurately reproduced the mean observed leaf area index.
Figure 3: ED2 PFT-level predictions of leaf area index (LAI) by model configuration and parameterization.
Different parameter combinations led to model outputs that had not only different bulk ecosystem properties but also qualitatively different plant communities (Figure 3). Most model configurations could produce either a forest that was persistently and completely dominated by early hardwood or pine, or a forest with some coexistence of multiple PFTs. The exceptions, again, were model configurations combining finite canopy radius and the two-stream radiative transfer; in these configurations, the only outcomes were immediate ecosystem collapse of all PFTs or survival and mono-dominance of pine.
Comparison of parameteric uncertainty within ensembles (colored bars, colored by model type) and “structural” uncertainty (variance in ensemble means; black bar) by output variable. Output variables are expressed as growing season averages for all years after 1910.
Take-away: Overall, structural uncertainty (variance in ensemble means across structures) is comparable or lower than parameter uncertainty (uncertainty within ensembles for a given structure). Parameter uncertainty varies significantly across model structures. Enabling trait plasticity increases variance in predictions of AGB. Enabling the finite canopy radius increases the variance in LAI predictions. GPP and NPP have the highest variance by far for the combination of finite canopy radius and two-stream radiative transfer.
PEcAn-liked parameter sensitivity and uncertainty analysis, by model type. Elasticity (a) is the normalized sensitivity of the model to a fixed change in the parameter. pvar (b) is the partial variance, which describes the overall contribution of the parameter to model predictive uncertainty based on the combination of parameter uncertainty and model sensitivity. Input parameter distributions are shown in Figure XXX.
Take-away: Model parameter uncertainty is sensitive to model configuration. For example, turning on the finite canopy radius with the two-stream canopy RTM increases sensitivity across many parameters.
(TODO)
This project funded by NSF grant. Cyberinfrastructure provided by Pacific Northwest National Laboratory (PNNL). Data from University of Michigan Biological Station (UMBS). Data from TRY (TODO: Specific TRY statement).
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\[ LAI_e = \omega LAI \]
\[ TAI_e = LAI_e + WAI \]
\[ TAI_{l} = \frac{TAI_e}{CAI} \]
Leaf (\(w_l\)) and wood (\(w_w\)) weights:
\[ w_l = \frac{LAI_e}{TAI_e} \] \[ w_w = 1 - w_l \]
Projected area, based on coefficients \(\phi_1\) and \(\phi_2\).
\[ a_{proj} = \phi_1 + \phi_2 \mu \]
\[ \lambda = \frac{a_{proj}}{\mu} \]
| ED Name | Description | unit_markdown | Raczka | Dietze | Ran |
|---|---|---|---|---|---|
mort1 |
Time-scale at which low-carbon balance plants die | years-1 | Carbon balance mortality | Mortality | no |
root_turnover_rate |
Temperature dependent rate of fine root loss | year-1 | Root turnover | Root turnover | yes |
growth_resp_factor |
Fraction of daily C gain lost to growth respiration | unitless (0-1) | Growth respiration | Growth Resp | yes |
stomatal_slope |
Slope between A and stomatal conductance (Leuning) | unitless | Stomatal sensitivity | Stomatal Slope | yes |
fineroot2leaf (q) |
Ratio of fine root to leaf biomass | unitless (mass ratio) | Root/Leaf carbon | Leaf:Root | yes |
r_fract |
Fraction of C storage to seed reproduction | unitless | Recruitment carbon | Reproduction? | yes |
f_labile |
Fraction of litter that goes to labile (fast) C pool | unitless (0-1) | Labile carbon | NA | yes |
water_conductance |
“Water availability factor” | m-2 a-1 (kg C root)-1 | Soil-plant water conductance | Water Cond | yes |
mort3 |
Density-independent (background) mortality rate | year-1 | Background mortality | NA | no |
SLA |
Specific leaf area | m2 kg-1 C | Specific leaf area | SLA | yes |
Rd0 |
Leaf dark respiration at 15 °C | ??? | Leaf respiration* | Leaf Resp* | yes |
dark_respiration_factor |
Ratio of leaf respiration to Vcmax | ??? | Leaf respiration* | Leaf Resp* | yes |
quantum_efficiency |
Farquhar model parameter (TODO) | mol CO2 (mol photons)-1 | Quantum efficiency | Quantum Eff. | yes |
Vcmax (Vm0) |
Maximum rate of CO2 carboxylation at 15 °C | μmol m-2 s-1 | Vcmax | Vcmax | yes |
root_respiration_rate (_factor) |
Root respiration rate at 15 °C | μmol CO2 (kg fineroot)-1 | Root respiration | NA | yes |
minimum_height |
Minimum height for plant reproduction | m | Minimum height | NA | no |
leaf_reflect_vis |
Leaf reflectance in visible range (400-700 nm) | unitless (0-1) | NA | NA | no |
leaf_reflect_nir |
Leaf reflectance in NIR range (700-2500 nm) | unitless (0-1) | NA | NA | no |
leaf_trans_vis |
Leaf transmittance in visible range (400-700 nm) | unitless (0-1) | NA | NA | no |
leaf_trans_nir |
Leaf transmittance in NIR range (700-2500 nm) | unitless (0-1) | NA | NA | no |
c2n_fineroot |
C:N ratio in fine roots | unitless (mass ratio) | NA | NA | yes |
c2n_leaf |
C:N ratio in leaves | unitless (mass ratio) | NA | NA | yes |
leaf_turnover_rate |
Temperature dependent rate of leaf loss (conifer only) | year-1 | NA | NA | yes |
leaf_width |
Mean leaf width (for boundary layer conductance) | m | NA | NA | yes |
mort2 |
C balance ratio at which mortality rapidly increases | unitless (mass ratio) | NA | NA | yes |
nonlocal_dispersal |
“Proportion of dispersal that is global” | unitless (0-1) | NA | NA | yes |
seedling_mortality |
Proportion of seed that dies and goes to litter pool | unitless (0-1) | NA | NA | yes |
Vm_low_temp |
Minimum temperature for photosynthesis | °C | NA | NA | yes |
This report was generated on 2019-07-01 16:38:29 using the following computational environment and dependencies:
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The current Git commit details are:
#> Local: master /Users/shik544/Box Sync/Projects/forte_project/fortebaseline
#> Remote: master @ origin (git@github.com:ashiklom/fortebaseline.git)
#> Head: [7dc6465] 2019-07-01: Add results text about lai x pft figure